# Feature Encoding with AutoEncoders for Weakly-supervised Anomaly Detection

arXiv: https://arxiv.org/abs/2105.10500 · HTML: https://mlsanity.com/paper/2105.10500/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 39 references that are in the map.
- First posted on arXiv 22 May 2021.

## Builds on (most cited first, 11 of 11)

- [Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection](https://mlsanity.com/paper/1904.02639/) (2019)
- [Deep Semi-Supervised Anomaly Detection](https://mlsanity.com/paper/1906.02694/) (2019)
- [Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications](https://mlsanity.com/paper/1802.03903/) (2018)
- [Deep Anomaly Detection with Deviation Networks](https://mlsanity.com/paper/1911.08623/) (2019)
- [Adversarially Learned Anomaly Detection](https://mlsanity.com/paper/1812.02288/) (2018)
- [Self-trained Deep Ordinal Regression for End-to-End Video Anomaly Detection](https://mlsanity.com/paper/2003.06780/) (2020)
- [Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection](https://mlsanity.com/paper/1806.04808/) (2018)
- [Deep Weakly-supervised Anomaly Detection](https://mlsanity.com/paper/1910.13601/) (2019)
- [Unsupervised and Semi-supervised Anomaly Detection with LSTM Neural Networks](https://mlsanity.com/paper/1710.09207/) (2017)
- [Active Learning for Wireless IoT Intrusion Detection](https://mlsanity.com/paper/1808.01412/) (2018)
- [Deep Anomaly Detection in Packet Payload](https://mlsanity.com/paper/1912.02549/) (2019)

## Built on by (most cited first, 30 of 42)

- [ADBench: Anomaly Detection Benchmark](https://mlsanity.com/paper/2206.09426/) (2022)
- [ImbSAM: A Closer Look at Sharpness-Aware Minimization in Class-Imbalanced Recognition](https://mlsanity.com/paper/2308.07815/) (2023)
- [MultiOOD: Scaling Out-of-Distribution Detection for Multiple Modalities](https://mlsanity.com/paper/2405.17419/) (2024)
- [Weakly Supervised Anomaly Detection: A Survey](https://mlsanity.com/paper/2302.04549/) (2023)
- [NNG-Mix: Improving Semi-supervised Anomaly Detection with Pseudo-anomaly Generation](https://mlsanity.com/paper/2311.11961/) (2023)
- [Clustering Aided Weakly Supervised Training to Detect Anomalous Events in Surveillance Videos](https://mlsanity.com/paper/2203.13704/) (2022)
- [Unsupervised Deep Learning for IoT Time Series](https://mlsanity.com/paper/2302.03284/) (2023)
- [RoSAS: Deep Semi-Supervised Anomaly Detection with Contamination-Resilient Continuous Supervision](https://mlsanity.com/paper/2307.13239/) (2023)
- [ADGym: Design Choices for Deep Anomaly Detection](https://mlsanity.com/paper/2309.15376/) (2023)
- [Weakly Supervised Anomaly Detection via Knowledge-Data Alignment](https://mlsanity.com/paper/2402.03785/) (2024)
- [Anomaly Detection with Score Distribution Discrimination](https://mlsanity.com/paper/2306.14403/) (2023)
- [FedTADBench: Federated Time-Series Anomaly Detection Benchmark](https://mlsanity.com/paper/2212.09518/) (2022)
- [Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction](https://mlsanity.com/paper/2502.10211/) (2025)
- [Normality-Calibrated Autoencoder for Unsupervised Anomaly Detection on Data Contamination](https://mlsanity.com/paper/2110.14825/) (2021)
- [ADPS: Asymmetric Distillation Post-Segmentation for Image Anomaly Detection](https://mlsanity.com/paper/2210.10495/) (2022)
- [Towards Personalized Quantum Federated Learning for Anomaly Detection](https://mlsanity.com/paper/2511.07471/) (2025)
- [ICLAD: In-Context Learning for Unified Tabular Anomaly Detection Across Supervision Regimes](https://mlsanity.com/paper/2603.19497/) (2026)
- [DTAAD: Dual Tcn-Attention Networks for Anomaly Detection in Multivariate Time Series Data](https://mlsanity.com/paper/2302.10753/) (2023)
- [Uncertainty-aware Evaluation of Auxiliary Anomalies with the Expected Anomaly Posterior](https://mlsanity.com/paper/2405.13699/) (2024)
- [Deep Positive-Unlabeled Anomaly Detection for Contaminated Unlabeled Data](https://mlsanity.com/paper/2405.18929/) (2024)
- [Prototypical Learning Guided Context-Aware Segmentation Network for Few-Shot Anomaly Detection](https://mlsanity.com/paper/2512.15319/) (2025)
- [AnoOnly: Semi-Supervised Anomaly Detection with the Only Loss on Anomalies](https://mlsanity.com/paper/2305.18798/) (2023)
- [Outlier detection using flexible categorisation and interrogative agendas](https://mlsanity.com/paper/2312.12010/) (2023)
- [Multi-Normal Prototypes Learning for Weakly Supervised Anomaly Detection](https://mlsanity.com/paper/2408.14498/) (2024)
- [Classifier-Free Diffusion-Based Weakly-Supervised Approach for Health Indicator Derivation in Rotating Machines: Advancing Early Fault Detection and Condition Monitoring](https://mlsanity.com/paper/2409.01676/) (2024)
- [Swift Hydra: Self-Reinforcing Generative Framework for Anomaly Detection with Multiple Mamba Models](https://mlsanity.com/paper/2503.06413/) (2025)
- [Text-ADBench: Text Anomaly Detection Benchmark Based on LLM Embeddings](https://mlsanity.com/paper/2507.12295/) (2025)
- [Deep Learning Approach to Anomaly Detection in Enterprise ETL Processes with Autoencoders](https://mlsanity.com/paper/2511.00462/) (2025)
- [SetAD: Semi-Supervised Anomaly Learning in Contextual Sets](https://mlsanity.com/paper/2512.07863/) (2025)
- [Zero-shot domain adaptation of anomalous samples for semi-supervised anomaly detection](https://mlsanity.com/paper/2304.02221/) (2023)

## Abstract (arXiv)

Weakly-supervised anomaly detection aims at learning an anomaly detector from a limited amount of labeled data and abundant unlabeled data. Recent works build deep neural networks for anomaly detection by discriminatively mapping the normal samples and abnormal samples to different regions in the feature space or fitting different distributions. However, due to the limited number of annotated anomaly samples, directly training networks with the discriminative loss may not be sufficient. To overcome this issue, this paper proposes a novel strategy to transform the input data into a more meaningful representation that could be used for anomaly detection. Specifically, we leverage an autoencoder to encode the input data and utilize three factors, hidden representation, reconstruction residual vector, and reconstruction error, as the new representation for the input data. This representation amounts to encode a test sample with its projection on the training data manifold, its direction to its projection and its distance to its projection. In addition to this encoding, we also propose a novel network architecture to seamlessly incorporate those three factors. From our extensive experiments, the benefits of the proposed strategy are clearly demonstrated by its superior performance over the competitive methods.